This invention pertains to analog-to-digital converters, particularly as implemented in a neural network.
Analog-to-digital converters are used in a wide variety of applications. Existing analog-to-digital converters are primarily of four types (although this listing is not intended to be exclusive): counter, successive approximation, integrating, and flash. A counter is relatively slow, and can take up to 2.sup.n steps for an n-bit conversion, with an average of 2.sup.n -1 steps. A successive approximation converter uses a single R2R resistor ladder, and takes n steps to make an n-bit conversion. An integrating converter, which measures the time elapsed until an increasing voltage ramp reaches the voltage of the input, is relatively accurate, but is expensive and is considerably slower than a successive approximation converter. A flash converter is quite fast, accomplishing a conversion in essentially one step, but is expensive and requires 2.sup.n -1 comparators in a single circuit for an n-bit conversion.
A neural network is a set of computational units whose interconnections are analogous to biological neurons. In general, any system whose interconnections resemble in some way those of biological neurons may be called a neural network. Each computational unit comprises a neuron, one or more inputs, and one or more outputs. An input for a neuron may be connected to the output of one or more other neurons. In some cases, a neuron may feedback on itself by connecting one of its inputs to one of its own outputs.
Classical neural networks are those which learn facts or patterns, and show associative recall of the stored patterns. J. Hopfield and D. Tank, "Computing with Neural Circuits: A Model," Science, Aug. 1986, Vol 233, pp 625-32, demonstrated the associative recall capability of such a neural network. Some neural network models have departed from the classical models, and have assumed a form more specific to the nature of the problem. For example, D. Tank and J. Hopfield, "Simple `Neural` Optimization Networks: An A/D Converter, Signal Decision Circuit, and a Linear Programming Circuit," IEEE Transactions on Circuits and Systems, Vol CAS-33, No. 5, May 1986, pp 533-541, gave a model for a synchronous analog-to-digital converter circuit. The resistance values of the Tank and Hopfield converter are somewhat difficult to implement, and would become unwieldy for a large number of bits. The Hopfield circuit also requires both negative and positive biases, and requires a relatively large number of components. The circuit dynamics are based on analog dynamics.
Lee and Sheu, "An Investigation of Local Minima of Hopfield Network for Optimization Circuits," IEEE International Conference on Neural Networks, pp I-45 to 51 (July 1988) give a modification of the Tank and Hopfield circuit. This circuit is essentially similar to that of Tank and Hopfield, with the addition of some correction circuitry to overcome some of the problems encountered by the Tank and Hopfield circuit.